Modality Prompts for Arbitrary Modality Salient Object Detection
- URL: http://arxiv.org/abs/2405.03351v1
- Date: Mon, 6 May 2024 11:02:02 GMT
- Title: Modality Prompts for Arbitrary Modality Salient Object Detection
- Authors: Nianchang Huang, Yang Yang, Qiang Zhang, Jungong Han, Jin Huang,
- Abstract summary: This paper delves into the task of arbitrary modality salient object detection (AM SOD)
It aims to detect salient objects from arbitrary modalities, eg RGB images, RGB-D images, and RGB-D-T images.
A novel modality-adaptive Transformer (MAT) will be proposed to investigate two fundamental challenges of AM SOD.
- Score: 57.610000247519196
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This paper delves into the task of arbitrary modality salient object detection (AM SOD), aiming to detect salient objects from arbitrary modalities, eg RGB images, RGB-D images, and RGB-D-T images. A novel modality-adaptive Transformer (MAT) will be proposed to investigate two fundamental challenges of AM SOD, ie more diverse modality discrepancies caused by varying modality types that need to be processed, and dynamic fusion design caused by an uncertain number of modalities present in the inputs of multimodal fusion strategy. Specifically, inspired by prompt learning's ability of aligning the distributions of pre-trained models to the characteristic of downstream tasks by learning some prompts, MAT will first present a modality-adaptive feature extractor (MAFE) to tackle the diverse modality discrepancies by introducing a modality prompt for each modality. In the training stage, a new modality translation contractive (MTC) loss will be further designed to assist MAFE in learning those modality-distinguishable modality prompts. Accordingly, in the testing stage, MAFE can employ those learned modality prompts to adaptively adjust its feature space according to the characteristics of the input modalities, thus being able to extract discriminative unimodal features. Then, MAFE will present a channel-wise and spatial-wise fusion hybrid (CSFH) strategy to meet the demand for dynamic fusion. For that, CSFH dedicates a channel-wise dynamic fusion module (CDFM) and a novel spatial-wise dynamic fusion module (SDFM) to fuse the unimodal features from varying numbers of modalities and meanwhile effectively capture cross-modal complementary semantic and detail information, respectively. Moreover, CSFH will carefully align CDFM and SDFM to different levels of unimodal features based on their characteristics for more effective complementary information exploitation.
Related papers
- AMM-Diff: Adaptive Multi-Modality Diffusion Network for Missing Modality Imputation [2.8498944632323755]
In clinical practice, full imaging is not always feasible, often due to complex acquisition protocols, stringent privacy regulations, or specific clinical needs.
A promising solution is missing data imputation, where absent modalities are generated from available ones.
We propose an Adaptive Multi-Modality Diffusion Network (AMM-Diff), a novel diffusion-based generative model capable of handling any number of input modalities and generating the missing ones.
arXiv Detail & Related papers (2025-01-22T12:29:33Z) - MAGIC++: Efficient and Resilient Modality-Agnostic Semantic Segmentation via Hierarchical Modality Selection [20.584588303521496]
We introduce the MAGIC++ framework, which comprises two key plug-and-play modules for effective multi-modal fusion and hierarchical modality selection.
Our method achieves state-of-the-art performance on both real-world and synthetic benchmarks.
Our method is superior in the novel modality-agnostic setting, where it outperforms prior arts by a large margin.
arXiv Detail & Related papers (2024-12-22T06:12:03Z) - Unsupervised Modality Adaptation with Text-to-Image Diffusion Models for Semantic Segmentation [54.96563068182733]
We propose Modality Adaptation with text-to-image Diffusion Models (MADM) for semantic segmentation task.
MADM utilizes text-to-image diffusion models pre-trained on extensive image-text pairs to enhance the model's cross-modality capabilities.
We show that MADM achieves state-of-the-art adaptation performance across various modality tasks, including images to depth, infrared, and event modalities.
arXiv Detail & Related papers (2024-10-29T03:49:40Z) - DMM: Disparity-guided Multispectral Mamba for Oriented Object Detection in Remote Sensing [8.530409994516619]
Multispectral oriented object detection faces challenges due to both inter-modal and intra-modal discrepancies.
We propose Disparity-guided Multispectral Mamba (DMM), a framework comprised of a Disparity-guided Cross-modal Fusion Mamba (DCFM) module, a Multi-scale Target-aware Attention (MTA) module, and a Target-Prior Aware (TPA) auxiliary task.
arXiv Detail & Related papers (2024-07-11T02:09:59Z) - MMA-DFER: MultiModal Adaptation of unimodal models for Dynamic Facial Expression Recognition in-the-wild [81.32127423981426]
Multimodal emotion recognition based on audio and video data is important for real-world applications.
Recent methods have focused on exploiting advances of self-supervised learning (SSL) for pre-training of strong multimodal encoders.
We propose a different perspective on the problem and investigate the advancement of multimodal DFER performance by adapting SSL-pre-trained disjoint unimodal encoders.
arXiv Detail & Related papers (2024-04-13T13:39:26Z) - Unleashing Network Potentials for Semantic Scene Completion [50.95486458217653]
This paper proposes a novel SSC framework - Adrial Modality Modulation Network (AMMNet)
AMMNet introduces two core modules: a cross-modal modulation enabling the interdependence of gradient flows between modalities, and a customized adversarial training scheme leveraging dynamic gradient competition.
Extensive experimental results demonstrate that AMMNet outperforms state-of-the-art SSC methods by a large margin.
arXiv Detail & Related papers (2024-03-12T11:48:49Z) - Exploiting Modality-Specific Features For Multi-Modal Manipulation
Detection And Grounding [54.49214267905562]
We construct a transformer-based framework for multi-modal manipulation detection and grounding tasks.
Our framework simultaneously explores modality-specific features while preserving the capability for multi-modal alignment.
We propose an implicit manipulation query (IMQ) that adaptively aggregates global contextual cues within each modality.
arXiv Detail & Related papers (2023-09-22T06:55:41Z) - Exploiting modality-invariant feature for robust multimodal emotion
recognition with missing modalities [76.08541852988536]
We propose to use invariant features for a missing modality imagination network (IF-MMIN)
We show that the proposed model outperforms all baselines and invariantly improves the overall emotion recognition performance under uncertain missing-modality conditions.
arXiv Detail & Related papers (2022-10-27T12:16:25Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.